Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images

• URL: http://arxiv.org/abs/2102.05450v1
• Date: Wed, 10 Feb 2021 14:12:48 GMT
• Title: Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images
• Authors: Madhu Mithra K K, Sriprabha Ramanarayanan, Keerthi Ram, Mohanasankar Sivaprakasam
• Abstract summary: We propose a reference-based, unpaired multi-contrast texture-transfer strategy for deep learning based in-plane and across-plane MRI super-resolution. We apply our scheme in different super-resolution architectures, observing improvement in PSNR and SSIM for 4x super-resolution in most of the cases.
• Score: 1.978587235008588
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Magnetic Resonance Imaging (MRI) is a valuable clinical diagnostic modality for spine pathologies with excellent characterization for infection, tumor, degenerations, fractures and herniations. However in surgery, image-guided spinal procedures continue to rely on CT and fluoroscopy, as MRI slice resolutions are typically insufficient. Building upon state-of-the-art single image super-resolution, we propose a reference-based, unpaired multi-contrast texture-transfer strategy for deep learning based in-plane and across-plane MRI super-resolution. We use the scattering transform to relate the texture features of image patches to unpaired reference image patches, and additionally a loss term for multi-contrast texture. We apply our scheme in different super-resolution architectures, observing improvement in PSNR and SSIM for 4x super-resolution in most of the cases.

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